The Problem: More Emails, Slower Responses
A B2B trading team received 50-70 buyer inquiries every week. At first, it was manageable, but after three months, over 500 inquiries had accumulated in Google Sheets.
"When new inquiries arrive, the old ones are automatically forgotten. We don't know which is urgent, and response order becomes chaotic."
The team noticed response time was falling, with high-value buyers waiting over 3 days for replies.
Automation Design: Building a Priority Engine
Bella created an automation bot using Claude Code with the following rules embedded:
1. First-time buyers (new) > repeat inquiries
2. Order size detection (bulk order keywords)
3. Language complexity (Chinese > English)
4. Time elapsed since inquiry
Each email was automatically assigned a priority score of 1-5.
Execution: Windows and Mac mini Task Division
Windows Device (Main Operations)
• Read inquiry data from Google Sheets
• Execute priority algorithm via Claude Code
• Auto-record results in new column (Priority_Score)
Mac mini Bot (Secondary Validation)
• Verify data after Windows task completion
• Flag priority errors
• Generate weekly validation reports
Results: 60% Response Time Cut
Before (Manual)
• Time to prioritize first 100 of 500 inquiries: 4 hours
• Average response waiting time: 3.2 days
• High-value inquiries missed per month: 5-7
After (Automated)
• Time to prioritize 500 inquiries: 12 minutes (API costs included)
• Average response waiting time: 1.3 days (60% reduction)
• Inquiries missed per month: 0-1 (95% improvement)
• Bonus effect: Buyer re-engagement rate increased 43% after faster responses
An Unexpected Finding
While running the automation, one pattern emerged. Inquiries arriving Monday morning between 6-8 AM (Korean time) had a 3x higher response rate than other times.
"That's when Southeast Asian buyers check their emails early. Now we prioritize replies during that window."
The algorithm didn't just categorize, it improved the team's entire workflow.